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BERTopicBERTopic is a neural topic-modeling pipeline introduced by Maarten Grootendorst in 2022. It combines BERT-based contextual embeddings with UMAP dimensionality reduction and HDBSCAN域自适应非负矩阵分解主题模型Domain-adaptive NMF Topic Modeling applies Non-negative Matrix Factorization to discover latent topics across text from multiple domains, using regularization or shared basis const可解释的LDA主题模型Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretabi可解释的非负矩阵分解主题模型An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such a可解释主题建模Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence score微调LDA主题模型Fine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather
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